Materials science & engineering. C, Materials for biological applications
30678956
In this study, a high-performance biosensing nanoplatform based on amidoxime-modified polyacrylonitrile nanofibers decorated with Ag nanoparticles (AgNPs-PAN-oxime NFs) is described. The AgNPs-PAN-oxime NFs were prepared by the combination of electro...
Cancer treatment and research communications
30207285
BACKGROUND: Measurement of autoantibodies (AAbs) to tumor associated antigens has been proposed to aid in the early detection of ovarian cancer with high specificity. Here we describe a multiplex approach to evaluate selected peptide epitopes of p53 ...
OBJECTIVES: To determine the risk of endometrial cancer (EC) and lymph node involvement in patients with a preoperative diagnosis of "AH-only" versus "AH - cannot rule out carcinoma" and to study the value of SLN mapping.
The development of a novel flexible and ultrasensitive aptasensor based on carboxylated multiwalled carbon nanotubes (MWCNTs)/ reduced graphene oxide-based field effect transistor (FET) has been reported for label-free detection of the ovarian cancer...
OBJECTIVES: The aim of the study was to assess the role of HE4 and CA125 in differentiation between malignant and non-malignant endometrial pathologies.
BACKGROUND: Previously proposed criteria for preoperatively identifying endometrial cancer patients at low risk for lymph node metastasis remain to be verified. For this purpose, a prospective, multicenter observational study was performed.
We evaluated the application of three machine learning algorithms, including logistic regression, support vector machine and back-propagation neural network, for diagnosing congenital heart disease and colorectal cancer. By inspecting related serum t...
Journal of the American College of Radiology : JACR
29789232
PURPOSE: The aim of this study was to use machine learning to predict abdominal recurrence on CT on the basis of serial cancer antigen 125 (CA125) levels in patients with advanced high-grade serous ovarian cancer on surveillance.
OBJECTIVES: The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.
BACKGROUND: The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.